Design and Implementation of Anti Web DDoS Attack Model Based on Improved Logistic Regression Algorithm

信息网络安全 ›› 2017, Vol. 17 ›› Issue (6): 62-67.doi: 10.3969/j.issn.1671-1122.2017.06.010

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Design and Implementation of Anti Web DDoS Attack Model Based on Improved Logistic Regression Algorithm

ZHANG Xuebo¹, LIU Jinghao¹, FU Xiaomei²

1. School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China;
2.School of Marine Science and Technology, Tianjin University, Tianjin 300072, China

Received:2017-05-05 Online:2017-06-20

Abstract

Abstract: Web DDoS attack has become one of the common ways for hackers to attack. In order to improve the detection speed and accuracy of Web DDoS attack effectively, this paper proposes a light weight and novel detection algorithm combined quantum particle swarm optimization method with Logistic regression model. This algorithm replaces Newton method with adaptive swarm optimization method to solve Logistic regression coefficient, improving the efficiency and accuracy of solving the regression coefficient. In order to verify the availability of the proposed algorithm, the WorldCup98 open dataset was used in our study to compare the performance of our algorithm with the existing improved Logistic regression algorithms.The experimental results show that compared with the existing improved Logistic regression algorithm, the proposed algorithm has higher detection rate and smaller detection error rate in terms of detecting Web DDoS attacks. Meanwhile,there is a linear relationship between the time complexity of the proposed algorithm and the number of detection sample.

Key words: Web DDoS attack detection, Logistic regression, quantum particle swarm optimization algorithm, Newton method

CLC Number:

TP393

ZHANG Xuebo, LIU Jinghao, FU Xiaomei. Design and Implementation of Anti Web DDoS Attack Model Based on Improved Logistic Regression Algorithm[J]. 信息网络安全, 2017, 17(6): 62-67.

References

[1] WEI Y, LEE M.Defending Application DDoS with Constraint Random Request Attacks [C]// IEEE. Communications, 2005 Asia-Pacific Conference on, October 5,2005,Perth, WA, Australia. New York: IEEE,2005:620-624.
[2] YU S, ZHOU Wanlei, JIA Weijia, et al. Traceback of DDoS Attacks Using Entropy Variations[J].IEEE Transactions on Parallel and Distributed Systems, 2011,22(3):412-425.
[3] 赵呈亮,李爱平,江荣. 基于TOPSIS-GRA集成评估法的DDoS攻击效果评估技术研究[J]. 信息网络安全,2016（10）：40-46.
[4] 田正先.基于网络效用最大化的DDoS攻击主动防御机制研究[D].武汉:华中科技大学,2011.
[5] 赵利明.基于路由协作的DDoS检测与防御研究[D].沈阳:东北大学,2011.
[6] YAAR A, PERRIG A, SONG D.StackPi:New Packet Marking and Filtering Mechanisms for DDoS and IP Spoofing Defense[J].IEEE Journal on Selected Areas in Communications, 2006,24(10):1853-1863.
[7] YAU D, LUI J, LIANG F.Defending Against Distributed Denial-of-Service Attacks with Max-Min Fair Server-Centric Router Throttles[J].Tenth IEEE International Workshop on Quality of Service,2002,13(1):35-44.
[8] 郑镇旭.韩国遭受DDoS网络攻击[EB/OL].http://world.kbs.co.kr/chinese/program/program_economyplus_detail.htm?No=1813, 2009-10-15.
[9] 王晓易.中国".CN"域名遭大规模攻击或是有组织攻击[EB/OL].http://war.163.com/13/0828/08/97BPF01O00014OMD.html, 2013-10-15.
[10] 百度安全.2016年DDoS攻击报告[R].北京：百度安全,2017.
[11] 孙未,张亚平.基于用户忠实度的应用层DDoS防御模型[J].计算机工程与设计,2015,36(1):93-97.
[12] 王进,阳晓龙,隆克平.基于大偏差统计模型的Http-Flood DDoS检测机制及性能分析[J].软件学报,2012,34(5):1272-1280.
[13] XU C, ZHAO G F, XIE G G, et al. Detection on Application Layer DDoSUsing Random Walk Model[J].Communication and Information Systems Security Symposium,2014:707-712.
[14] XIE Yi, YU S Z.A Large-Scale Hidden Semi-Markov Model for Anomaly Detection on User Browsing Behaviors[J].IEEE/ACM Transactions on Networking 2009,17(1):54-65.
[15] 赵国锋,喻守成,文晟.基于用户行为分析的应用层DDoS攻击检测方法[J].计算机应用研究,2011,28(2):717-719.
[16] 肖军,云晓春,张永铮.基于会话异常度模型的应用层分布式拒绝服务攻击过滤[J].计算机学报,2010,33(9):1713-1724.
[17] 嵆海进,蔡明.基于可信度的应用层DDoS攻击防御方法[J].计算机工程与设计,2007,28(19):4619-4621.
[18] TAN P N, STENINBACH M, KUMA V. Introduction to Data Mining[M]. Boston: Addison Wesley,2005.
[19] MINKA TP.A Comparison of Numerical Optimizers for Logistic Regression[J].Journal of the American Chemical Society.2003,125(6):1660-1668.
[20] VAN D, ENGELBRECHT A. A New Locally Convergent Particle Swarm Optimizer[C]//IEEE. 2002 IEEE Internation Conference on system,Man and Cybernetics,October 6-9,2002. Yasmine Hammamet, Tunisia. New York: IEEE,2002: 6-23.
[21] SUN J, FENG B, XU W.A Global Search Strategy of Quantum-Behaved Particle Swarm Optimization[C]//IEEE. 2004 IEEE conference on Cybernetics and Intelligent Systems, December 1-3,2004,Singapore. New York: IEEE,2004:111-116.
[22] 方伟,孙俊,谢振平,等.量子粒子群优化算法的收敛性分析及控制参数研究[J].物理学报,2010,59(6):3686-3694.
[23] HUSSAIN T, ASGHAR S, MASOOD N. Web Usage Mining：A Survey on Preprocessing of Web Log File[C]// IEEE .2010 International Conference on Information and Emerging Technologies. June 14-16 ,2010,Karachi, Pakistan. New York: IEEE,2010:1-6.
[24] 张选平,杜玉平,秦国强,等.一种动态改变惯性权的自适应粒子群算法[J].西安交通大学学报,2005,39(10):1039-1042.
[25] 胡洋瑞,陈兴蜀,王俊峰,等. 基于流量行为特征的异常流量检测[J]. 信息网络安全,2016（11）：45-51.
[26] 1998 World Cup Web Site Access Logs[EB/OL].http://ita.ee.lbl.gov/html/contrib/WorldCup.html,2016-5-15.
[27] KOMAREK P, MOORE AW. Making Logistic Regression a Core Data Mining Tool with tr-irls[C]//IEEE. ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining, November 27-30,2005,Houston, TX, USA. New York: IEEE,2005:685-688.

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